Minor fixes and tweaks based on feedback.

Author: rishal-hurbans

ch02-search_fundamentals/uninformed_search/maze_bfs.py

@@ -49,7 +49,8 @@ def is_in_visited_points(current_point, visited_points):
 maze_game_main = mp.MazePuzzle()
 
 # Run the breadth first search algorithm with the initialized maze
-outcome = run_bfs(maze_game_main, mp.Point(2, 2), [])
+starting_point = mp.Point(2, 2)
+outcome = run_bfs(maze_game_main, starting_point, [])
 
 # Get the path found by the breadth first search algorithm
 bfs_path = mp.get_path(outcome)

ch02-search_fundamentals/uninformed_search/maze_dfs.py

@@ -49,7 +49,8 @@ def is_in_visited_points(current_point, visited_points):
 maze_game_main = mp.MazePuzzle()
 
 # Run the depth first search algorithm with the initialized maze
-outcome = run_dfs(maze_game_main, mp.Point(2, 2))
+starting_point = mp.Point(2, 2)
+outcome = run_dfs(maze_game_main, starting_point)
 
 # Get the path found by the depth first search algorithm
 dfs_path = mp.get_path(outcome)

ch02-search_fundamentals/uninformed_search/maze_puzzle.py

@@ -1,4 +1,5 @@
 import copy
+import math
 
 
 # This class is used to store the idea of a point in the maze and linking it to other points to create a path.
@@ -7,7 +8,7 @@ def __init__(self, x=0, y=0):
         self.x = x
         self.y = y
         self.parent = None
-        self.cost = 99999
+        self.cost = math.inf
 
     def set_parent(self, p):
         self.parent = p
@@ -50,8 +51,8 @@ def get_current_point_value(self, current_point):
     # Return all valid neighbors around a specific point, excluding points outside of the maze and walls.
     def get_neighbors(self, current_point):
         neighbors = []
-        # potential_neighbors = [[0, -1], [1, 0], [0, 1], [-1, 0]]
-        potential_neighbors = [[SOUTH.x, SOUTH.y], [WEST.x, WEST.y], [NORTH.x, NORTH.y], [EAST.x, EAST.y]]
+        # potential_neighbors = [[0, 1], [0, -1], [1, 0], [-1, 0]]
+        potential_neighbors = [[NORTH.x, NORTH.y], [SOUTH.x, SOUTH.y], [EAST.x, EAST.y], [WEST.x, WEST.y]]
         for neighbor in potential_neighbors:
             target_point = Point(current_point.x + neighbor[0], current_point.y + neighbor[1])
             if 0 <= target_point.x < self.maze_size_x and 0 <= target_point.y < self.maze_size_y:
@@ -131,12 +132,16 @@ def get_direction(origin, target):
         return 'S'
     elif target.x == origin.x + 1 and target.y == origin.y:
         return 'E'
-    else:
+    elif target.x == origin.x - 1 and target.y == origin.y:
         return 'W'
 
 
 # Utility to determine the cost of a move given a direction. In this case, North and South is 5, and East and West is 1.
+STANDARD_COST = 1
+GRAVITY_COST = 5
+
+
 def get_cost(direction):
     if direction == 'N' or direction == 'S':
-        return 5
-    return 1
+        return GRAVITY_COST
+    return STANDARD_COST

ch03-intelligent_search/adversarial_search/connect_ai.py

@@ -1,11 +1,12 @@
 import copy
 import random
+import math
 
 # Set the min-max IDs, and pseudo infinity constants
 MIN = -1
 MAX = 1
-INFINITY_POSITIVE = 999
-INFINITY_NEGATIVE = -999
+INFINITY_POSITIVE = math.inf
+INFINITY_NEGATIVE = -math.inf
 
 
 # This class contains a move and the respective value earned for that move

ch03-intelligent_search/adversarial_search/connect_ai_alpha_beta_pruning.py

@@ -1,11 +1,12 @@
 import copy
 import random
+import math
 
 # Set the min-max IDs, and pseudo infinity constants
 MIN = -1
 MAX = 1
-INFINITY_POSITIVE = 999
-INFINITY_NEGATIVE = -999
+INFINITY_POSITIVE = math.inf
+INFINITY_NEGATIVE = -math.inf
 
 
 # This class contains a move and the respective value earned for that move

ch03-intelligent_search/adversarial_search/connect_puzzle.py

@@ -36,7 +36,7 @@ def generate_board(self, board_size_x, board_size_y):
     def print_board(self):
         result = ''
         for y in range(0, self.board_size_y):
-            for x in range(self.board_size_x - 1, -1, -1):
+            for x in range(0, self.board_size_x):
                 result += self.board[x][y]
             result += '\n'
         print(result)
@@ -133,11 +133,13 @@ def execute_move(self, player, slot_number):
 
     # Execute a move for a player if there's space in the slot and choose the player based on whose turn it is
     def play_move(self, slot):
-        if not self.is_slot_full(slot):
-            if self.player_turn == PLAYERS[PLAYER_AI]:
-                self.execute_move(PLAYER_AI, slot)
-            else:
-                self.execute_move(PLAYER_HUMAN, slot)
-            self.player_turn *= -1
-            return True
+        if 0 <= slot <= 4:
+            if not self.is_slot_full(slot):
+                if self.player_turn == PLAYERS[PLAYER_AI]:
+                    self.execute_move(PLAYER_AI, slot)
+                else:
+                    self.execute_move(PLAYER_HUMAN, slot)
+                self.player_turn *= -1
+                return True
+            return False
         return False

ch03-intelligent_search/informed_search/maze_puzzle.py

@@ -1,4 +1,5 @@
 import copy
+import math
 
 
 # This class is used to store the idea of a point in the maze and linking it to other points to create a path.
@@ -7,7 +8,7 @@ def __init__(self, x=0, y=0):
         self.x = x
         self.y = y
         self.parent = None
-        self.cost = 99999
+        self.cost = math.inf
 
     def set_parent(self, p):
         self.parent = p
@@ -50,8 +51,8 @@ def get_current_point_value(self, current_point):
     # Return all valid neighbors around a specific point, excluding points outside of the maze and walls.
     def get_neighbors(self, current_point):
         neighbors = []
-        # potential_neighbors = [[0, -1], [1, 0], [0, 1], [-1, 0]]
-        potential_neighbors = [[SOUTH.x, SOUTH.y], [WEST.x, WEST.y], [NORTH.x, NORTH.y], [EAST.x, EAST.y]]
+        # potential_neighbors = [[0, 1], [0, -1], [1, 0], [-1, 0]]
+        potential_neighbors = [[NORTH.x, NORTH.y], [SOUTH.x, SOUTH.y], [EAST.x, EAST.y], [WEST.x, WEST.y]]
         for neighbor in potential_neighbors:
             target_point = Point(current_point.x + neighbor[0], current_point.y + neighbor[1])
             if 0 <= target_point.x < self.maze_size_x and 0 <= target_point.y < self.maze_size_y:
@@ -131,11 +132,15 @@ def get_direction(origin, target):
         return 'S'
     elif target.x == origin.x + 1 and target.y == origin.y:
         return 'E'
-    else:
+    elif target.x == origin.x - 1 and target.y == origin.y:
         return 'W'
 
 
 # Utility to determine the cost of a move given a direction. In this case, North and South is 5, and East and West is 1.
+STANDARD_COST = 1
+GRAVITY_COST = 5
+
+
 def get_cost(direction):
     if direction == 'N' or direction == 'S':
         return 5

ch04-evolutionary_algorithms/knapsack_brute_force.py

@@ -56,8 +56,8 @@ def get_all_combinations(items):
     combinations = []
     for index in range(0, len(items)):
         combinations.append(items[index])
-        els = [list(x) for x in itertools.combinations(items, index)]
-        combinations.append(els)
+        possibilities = [list(x) for x in itertools.combinations(items, index)]
+        combinations.append(possibilities)
     return combinations
 
 

ch04-evolutionary_algorithms/knapsack_genetic_algorithm.py

@@ -121,13 +121,17 @@ def set_probabilities(population):
 # Roulette wheel selection to select individuals in a population
 def roulette_wheel_selection(population, number_of_selections):
     set_probabilities(population)
+    slices = []
+    total = 0
+    for r in range(0, len(population)):
+        individual = population[r]
+        slices.append([r, total, total + individual[INDIVIDUAL_PROBABILITY_INDEX]])
+        total += individual[INDIVIDUAL_PROBABILITY_INDEX]
     chosen_ones = []
-    for selection in range(number_of_selections):
-        r = random.random()
-        for individual in population:
-            if r <= individual[INDIVIDUAL_PROBABILITY_INDEX]:
-                chosen_ones.append(individual)
-                break
+    for r in range(number_of_selections):
+        spin = random.random()
+        result = [s[0] for s in slices if s[1] < spin <= s[2]]
+        chosen_ones.append(population[result[0]])
     return chosen_ones
 
 

ch06-swarm_intelligence-ants/carnival_aco.py

@@ -8,8 +8,8 @@
 # least resistance.
 
 # Set the number of attractions in the data set
-# Best for 5 attractions: 19
-# Best for 48 attractions: 33523
+# Best total distance for 5 attractions: 19
+# Best total distance for 48 attractions: 33523
 ATTRACTION_COUNT = 48
 # Initialize the 2D matrix for storing distances between attractions
 attraction_distances = []